Friday, August 31, 2012

Activity-dependent relocation of the axon initial segment fine-tunes neuronal excitability

Grubb, MS. Burrone, J. (2010) Activity-dependent relocation of the axon initial segment fine-tunes neuronal excitability. Nature 465: 1070-1074.

Labelling for AIS scaffolding protein ankyrin G shows that the AIS will shift distally when the neuron is over-excited. When excitation is removed AIS will shift back towards the soma.

Movement is governed by Calcium channels and is not influenced by spiking. T- and/or L- type VGCCs. Burst activation of neurons is necessary to get AIS to move as sparse activation does not have much of an effect. This is probably due to different peak-levels of calcium entry.

Patch-clamp shows that distal AIS reduces excitability. Change in threshold of 100 pA. (change in Rm accounts for only 43 pA of 100 pA). Distal AIS lowers spike rate, but large current inputs can overcome deecrease and catch up to proximal AIS.

In supplemental they conclude that this process is a shift in the input-output function of the neuron, as opposed to gain or scaling. In my model, decreasing gc by moving the AIS away would result in a scaling. The fact that more current allows it to catch-up is result of spiking reaching saturation.

Monday, August 27, 2012

Sensation in a Single Neuron Pair Represses Male Behavior in Hermaphrodites

White, JQ. Jorgensen, EM. (2012) Sensation in a Single Neuron Pair Represses Male Behavior in Hermaphrodites. Neuron 75: 593-600.

Figure 1: Males are attracted to sex pheromones - daf-22-independent.WT herms avoid the pheromone. herms with daf-7 mutation are attracted to pheromones. Sensory neurons, ASK, AWA, AWC are needed (but they can compensate for each other).

Figure 2: ASI is only source of daf-7. Ablation of ASI shows attraction in herms. tax-4 expression in ASI is also needed for ASI to repress attraction. osm-3 mutation also cause herms to be attracted to pheromones. tax-4 is involved in ASI development and activity. osm-3 is involved in dendrite development of ASI.
For ASI to repress attraction, ASI must be:
1. present
2. active
3. capable of sensing environment

Expression of daf-7 in AWC and ASE neurons rescues pheromone avoidance in herms. ASI is not needed in the rescues to suppress attraction. Other manipulations show that ASI normally releases daf-7/tgf-beta and suppresses attraction. Can be bypassed by expressing daf-7 elsewhere or other manipulations.

Figure 3: expressing FEM-3 during development "masculinizes" the worms. herms become attracted to pheromones - doesnt happen if masculinized during adulthood.

Figure 4: can masculinize individual neurons. Can target sensory neurons (ASI/AWA/AWC/ASK) and different sets of interneurons (AIA, AIB, AIY, AIZ, RMG) with different promotors. To get attraction in herms, must masculinize both sensory and interneurons. Important to masculinize RMG.

Sunday, August 26, 2012

Neuromodulatory State and Sex Specify Alternative Behaviors through Antagonistic Synaptic Pathways in C. elegans

Jang, H. Kim, K. Neal, SJ. Macosko, E. Kim, D. Butcher, RA. Zeiger, DM. Bargmann, CI. Sengupta, P. (2012) Neuromodulatory State and Sex Specify Alternative Behaviors through Antagonistic Synaptic Pathways in C. elegans. Neuron 75: 585-592.

C9: repulsed - WT hermaphrodites
      attracted - WT males
      neutral - social hermaphrodites (npr-1 mutants)

WT herm: ADL detects C9 drives repulsion through its chemical synapses.
Males: ADL responses diminished. ASK circuit antagonizes repulsion.
Social: ADL response antagonized by RMG hub-spoke circuit.

ocr-2 trpv channels? I guess these are the detectors of some chemicals, used to detect C9 and glycerol. Or this is how they signal downstream...

Figure 1 - knock out ocr-2, prevents C9 avoidance behavior. Rescued when ocr-2 is expressed in ADL. ADL shows calcium response to C9, goes away when ocr-2 is knocked out. Blocking chemical synpatic transmission from ADL prevents C9 avoidance.

Figure 2 - npr-1 mutants are less sensitive to C9 - neutral to low concentrations, but still avoid high-concentrations. RMG circuit competes with ADL chem synapses to push repulsion - additive competition. npr-1 reduces Calcium response of ADL - indirect effects of RMG on ADL. ADL neurons not effected when RMG gap junctions are blocked. Altering chem strength biases avoidance.
Paradox: ADL repulses C9, but involved in aggregation. Solution: ADL repulses through chem synapses, RMG attracts through electrical - aggregation pushes balance towards RMG circuit.
npr-1 acts acutely, turned on by heat-shock.

Figure 3 - ADL Ca responses reduced in amplitude and delayed in males. ASK antagonizes repulsion - males with killed ASK avoid, no change for herms that normally avoid. ADL can drive repulsion if synapses are strengthened in males. Repulsion is inhibited by ASK and RMG.

Figure 4 - npr-1 males are attracted to C9. Manipulating the other parts have similar effects. npr-1 mutants show bigger resopnses to C9 in ASK. ADL circuit activates avoidance, RMG/ASK inhibit avoidance. ASK can sense C3, and mixture of C3 and C9 is neutral to npr-1 herms.

model: ADL chem synapses promote repulsion, RMG gap-junction promotes attraction. "push-pull circuit motif." npr-1 inhibits RMG through unknown mechanism - "it could close the RMG gap junctions to disengage the entire hub-and-spike circuit." RMG potentiates ASK signaling and inhibits ADL signaling.

animals can change attraction/repulsion behaviors depending on behavioral state. Suggesting that npr-1 may be modulated. "reminiscent of the flexible circuit states of crustacean stomch cpgs and vertabrate spinal cord motor circuits."

RMG antagonizes ADL while facilitating ASK. "common network motif can perform distinct computations in ways that are not evident solely from anatomical wiring diagrams." (ASK Ca responses are downward, ADL's are updward).

Wednesday, August 22, 2012

Synaptic Depression and Cortical Gain Control

Abbott, LF. Varela, JA. Sen, K. Nelson, SB. (1997) Synaptic Depression and Cortical Gain Control. Science 275: 220-224.

Model of STD is implemented by decreasing excitatory amplitude by factor f, whenever there's a presynaptic spike. Amplitude recovers exponentially back to 1. This changes the total amount of current that enters the cell as a function of rate. This makes the neuron sensitive to relative changes in the rates of the presynaptic neurons. With depression a 50% change in a 100Hz input, and a 50% change in a 10Hz input has about the same effect on the output.
Synaptic depression in this sense broadens the tuning curve of neurons, but does not change the selectivity. Makes the neuron sensitive to changes in the inputs overall, rather than total input values.

Shunting Inhibition Controls the Gain Modulation Mediated by Asynchronous Neurotransmitter Release in Early Development

Volman, V. Levine, H. Sejnowski, TJ. (2010) Shunting Inhibition Controls the Gain Modulation Mediated by Asynchronous Neurotransmitter Release in Early Development. PLoS Computational Biology 6(11).

Terry is on my committee and a giant in comp-neuro. He's like the father of computational neuroscience.

This goes into the noise-based mechanisms of shunting inhibition. Asynchronoous release is the spreading of vesicle release over time. They model vesicles and probabilities that vesicles get released.

This paper isn't really about shunting inhibition modulating the gain, but rather the effect of the asynchronous release. They talk about different types of shunt regimes - high shunt, low-shunt - which have different effects. The primary driver of gain modulation, however, is the synaptic depression. This mechanism is a gain on the inputs and is not controlled by gaba.

Monday, August 20, 2012

A re-examination of the possibility of controlling the firing rate gain of neurons by balancing excitatory and inhibitory conductances

Capaday, C. (2002). A re-examination of the possibility of controlling the firing rate gain of neurons by balancing excitatory and inhibitory conductances. Exp Brain Res 143: 67-77.

"Put simply, the firing rate gain of an input to a neuron cannot be controlled by balancing excitatory and inhibitory conductances produced by other independent input pathways, or by the spatial distribution of excitation and inhibition across the neuron"

Complex 2-compartment model of alpha motorneuron. Several channels. Mainly he agrees with Holt and Koch, but does not address the same issues as my model. He shows how inhibitory feedback can change the gain, similar to how Daniel's model uses proportional inhibition to regulate the gain.

Friday, August 17, 2012

The Self-Tuning Neuron: Synaptic Scaling of Excitatory Synapses

Turrigiano, GG. (2008) The Self-Turning Neuron: Synaptic Scaling of Excitatory Synapses. Cell 135: 422-435.

This is a really important set of ideas. Neurons scaling their inputs to maintain stability is essential, and relieves many of the stability problems you get when trying to run simulations.

Dark side of LTP - enhancing a synapse will make it more likely to make a neuron fire. This will further enhance the synapse, creating a positive feed-back loop. Theoreticians have suggested that there must be plasticity mechanisms that counteract the destabilizing forces. Synaptic scaling is such a mechanism: Neurons sense their activity, integrate over some time-scale, and multiplicatively adjust their synapes globally to adjust their average firing rate to some set-point.

Homeostasis shown in culture, not seen yet in intact CNS. Many different ways to maintian homeostasis: inhibitory/excitatory synaptic strength, synapse number, "metaplasiticity", "instrinsic excitability".

Scaling allows neurons to normalize firing without changing relative strengths of synapses - thus avoiding disruption of info storage. Inhibitory synapse scale in opposite direction of excitatory synapses. Blocking APs at soma only (via TTX) is sufficient to induce scaling.

Scaling operates on both AMPA, and NMDA. Main mechanism is AMPA. Requires transcription - either produces more AMPA overall, or alters scaffolding proteins. Lots of possible molecular pathways. One of the ways to design the homeostatic feedback loop is with a calcium detector. Then this regulates the synaptic strength to reach a calcium set-point. Lots of other pathways: BDNF, TNFalpha, CAMKII). Not clear if there are several different types of pathways, or if these are interacting in a single pathway.

Scaling is important during development, seen in visual system. In layer 4 scaling turns off after the first postnatal week, and turns on in layer 2/3, where it can be induced into adulthood. Likely part of mechanism that alters responsiveness of neurons during visual blockade of one eye.

Scaling up vs. scaling down may have different mechanisms. Instead of one factor detecting deviations around the set point, there are 2. One detects above and then triggers the scale-down cascade, and the other detects below and triggers scale-up cascade.

Thursday, August 16, 2012

Computing with Neural Synchrony

Brette, R. (2012) Computing with Neural Synchrony. PLoS Computational Biology 8(6): 1-18.

Cites a lot of good papers on neural synchrony. Should look into these.

Neurons with "rebound spiking" - latency of spike depends on length of inhibition. When inhibition is released, spikes will be fast if neuron was inhibited for a long time, may not spike if short inhibition. The RF of these neurons is any stimuli which elicits a response - e.g. inhibition for more than 200 ms.

Synchrony Receptive Field (SRF): a stimuli which elicits synchronous firing in two neurons. Requires heterogeneous properties across neurons (otherwise, SRF would be all stimuli).
Synchrony detection depends on the time-constant - d' = w/s * (1 - exp(-d/t)), where w is synaptic weight, s is standard deviation of noise, d is delay between spikes and t is the neuron's time constant. Essentially the coincidence sensitivity are related to the neural time-constant. A low FA rate is important to get a high Hit rate with coincident spikes.

For learning need homeostatic mechanisms to keep a low FA rate - ala Turrigianno. Multiplicative scaling to maintain stable firing rate, F. Weights are learned via STDP, STDP time-constant is same as membrane time constant. Only uses LTP component of STDP, no LTD - relies on homeostasis for depression. 

Precise spike-timing is not important, but relative spike-timing is. The system can decodes relative spike-timing, and is able to maintain fairly precise relative spike timing. 
Synchrony coding has neurons fire when they are close to a hyperplane, as opposed to rate coding where they fire on one side of a hyperplane. There is robustness to variability with a synchrony code that a rate-like code does not handle. 

He goes on to show a lot more examples of specific systems that could be using a synchrony code and how they might work. Olfaction, binaural hearing, and some vision.

This is an interesting paper, adding in the gamma oscillation would allow a synchrony code to propagate. Also, having dendrites do the coincidence detection, and then combine for an output would be powerful. 

Wednesday, August 15, 2012

Parvalbumin-Expressing Interneruons Linearly Transform Cortical Responses to Visual Stimuli

Atallah, BV. Bruns, W. Carandini, M. Scanziani, M. (2012) Parvalbumin-Expressing Interneurons Linearly Transform Cortical Responses to Visual Stimuli. Neuron 73: 159-170.

Another paper from Massimo's lab. Bass is a good friend. He's in portugal now.

Archeorhodopsin and ChR2 expressed in PVs. Targeted loose-patch ephys. In Layer 2/3. Drifting gratings.

Different response properties of PV and Pyr. PV much more broadly tuned (orientation). Pyramids have a much faster saturation in their contrast response compared to PV.

PV greatly influences Pry spike-rate, but does not change orientation tuning much. There is some slight change that is significant, but this is mainly attributed to the slight increase in Pyr activity. Near the range around threshold can morph the responses slightly in a non-multiplicative fashion. However, this is small.
PV influence on Pyr can be described with a linear-threshold function (since Pyr rate > 0). There is both a scaling factor and a additive factor.

They have a conductance model that explains the tuning properties. This model is basically a rate-coded model. They calculate the equilibrium voltage from conductance measurements (thus conductance changes from PV), and then pass the voltage through a non-linear spiking function. No actual spikes are generated (by passing the Holt-Koch problem). The non-linear function may play an important role as it is exponential - the voltage changes look additive, but the exponential may be making it look multiplicative in the end.
This directly contradicts the Lee, Dan Paper. It does seem possible that strong activation of PV, as opposed to the more moderate activation here, could result in different properties (especially since there is a small additive component).

Monday, August 13, 2012

Activation of specific interneurons improves V1 feature selectivity and visual perception.

Lee, SH. Kwan, AC. Zhang, S. Phoumthipphavong, V. Flannery, JG. Masmanidis, SC. Taniguchi, H. Huang, ZJ. Zhang, F. Boyden, ES. Deisseroth, K. Dan, Y. (2012) Activation of specific interneurons improves V1 feature selectivity and visual perception. Nature.

ChR2 targeted to PV, SOM, VIP, CAMKII-Arch. Multichannel probe for recording all layers. Drifting grating.

Activation of PV caused decrease in tuning bandwidth, and increase in direction selectivity. Preferred orientation remained relatively constant. They record whole-cell, inject current to get F-I response and find that PV activation is additive, while SOM activation is multiplicative. (This is the opposite effects of what the other papers say about the roles of PV and SOM).

They next had an orientation discrimination task with mice and activated the PV cells. They found that activating the PV cells can actually improve discriminability (d') of the Go, No-Go task. They claim that this is because PV increases the orientation selectivity of neurons, making the task easier. (I'm skeptical of this, decreasing the tuning width is likely to remove information of the orientation from the system, it definitely does not add any information.)

They address Bass's result saying: Bass's study shows "PV activation only moderately affects the tuning of V1 neurons. This is probably caused by the relatively low level of PV activation compared with our study". So, this could mean the difference is explained by them over-activating PV cells and getting some iceberg effect.

Friday, August 10, 2012

Division and subtraction by distinct cortical inibitory networks in vivo.

Wilson, NR. Runyan, CA. Wang, FL. Sur, M. (2012) Division and subtraction by distinct cortical inhibitory networks in vivo. Nature

Optogenetic stimulation with in-vivo two-photon imaging. Custum scan-paths. ChR2 in Cre/loxP mice targeting PV or SOM. OGB calcium dye. Drifting gratings.

Using calcium responses can get tuning curves. Activation of PV shows a multiplicative effect, SOM shows additive effects (scale, vs shifting tuning curves). They did same experiments with cell-attached recordings of pyramidal cells, got essentially the same results.

They can reduce ChR2-stimulating beam to target a single PV neuron, while concurrently sampling population responses with Ca imaging. They detect different populations that are effected by inidividual neurons. About 43% in view were suppressed by PV, 16% by SOM.

No clear distance relationship with PV or SOM targeted networks - best fit by random connection model. There is a functional relationship, PV cells preferentially targeted pyramids that had similar orientation preference. SOM cells had no connection preference to similar oriented or orthogonal cells. (Does this imply a inh->exc learning rule?)

Thursday, August 9, 2012

Shunting Inhibition Does Not Have a Divisive Effect on Firing Rates

Holt, GR. Koch, C. (1997) Shunting Inhibition Does Not Have a Divisive Effect on Firing Rates. Neural Computation 9: 1001-1013.

Christof was my advisor in undergrad. He's famous for consciousness stuff as well as a bunch of computational biophysics stuff.

I was surprised there's so much in the literature about shunting inhibition not acting divisively. I'm working on a model in the leech where we use shunting inhibition divisively, and it's a pretty simple and biophysically plausible mechanism. I'll write some about that project later.

The shunting inhibition mechanism is important as we keep seeing gain control in cortex, but we have to make sure that it is being done correctly. Multiplicative inhibition is important because it can maintain a population code (the population vector points in the same direction), and keep activity in a good dynamic range. There could be additive feedback like mechanisms that are really in charge of gain control, but the simplest way would be to have a direct mechanism which would be a multiplicative synapse. Biophysically this is simply done through shunting inhibition, as shunting inhibition acts as a voltage divider. I'll discuss this more later too.

They use a fairly complex Neuron model with lots of different channel types. They also use an IF model. To simulate inhibition they change the leak conductance (this is fine).
They explain the reason it is additive is because the spiking mechanism clamps the voltage near the reversal potential. So the current from the leak channel is small. (This is what happened in our model too, actually. But to get over it you just have to add an extra compartment). I'm not quite sure why their detailed model of a neuron does lead to divisive changes - perhaps they're again doing the shunting within the same compartment as the spiking.

They make a cable equation model to look at the effect of distal inhibition and proximal excitation (for some reason). They show it is still additive. I'm not sure why they didn't do this with proximal inhibition and distal excitation - perhaps because then it would be divisive...

So the key difference between my model and this one is that they put the inhibitory synapse in the same compartment as the spiking mechanism.

Wednesday, August 8, 2012

Shunting Inhibition Modulates Neuronal Gain during Synaptic Excitation

Mitchell, SJ. Silver, RA. (2003). Shuniting Inhibition Modulates Neuronal Gain during Synaptic Excitation. Neuron 38: 443-445.

Several studies have suggested role of shunting inhibition as multiplicative gain or additive effects. Several studies show that tonic shunting inhibition does *not* regulate gain of neurons. This study suggests that fluctuating inhibition, however, can act divisively.

Used dynamic clamp in granule cells to pass in both excitatory conductances and inhibitory conductances. With varying excitation and tonic inhibition get gain and a slight shift, with tonic excitation and inhibition get only a shift.

Mean voltage is independent of tonic excitation. However, with synaptic excitation mean voltage changes in relation to excitation rate.

The absolute variability and frequency dependence of excitation variability are important for neuronal gain reductions during inhibition. Having a faster excitatory time-constant results in a large gain effect (as the input has high variability), while slower time constant looks more like a tonic input and results in a more additive effect via shunting.

Synaptic-inhibition also influences the gain, and can even act as gain control with tonic excitation.

Tuesday, August 7, 2012

Control of timing, rate and bursts of hippocampal place cells by dendritic and somatic inhibition

Royer, S. Zemelman, BV. Losonczy, A. Kim, Jinhyun. Chance, F. Magee, JC. Buzsaki, G. (2012) Control of timing, rate and bursts of hippocampal place cells by dendritic and somatic inhibition. Nature Neuroscience 15(5): 769-777.

In the place field, pyramidal neurons alternate between two distinct modes of discharge: single spikes and bursts of spikes.

Linear treadmill, local cues, place cells form. Silence PV and SOM cells with halorhodopsin in CA1. They classified all the cells based on certain spiking properties, and relation to theta and slow-wave ripples.

Pyramidal cells that have place fields within the segment of the track that was stimulated with light were facilitated when both PV and SOM were silenced. Out of field cells showed no effect, but also had no excitatory drive. PV and SOM have similar effects on spiking changes overall, but have "interleaved" effects - PV changes are largest at beginning of place field, and SOM are largest at end of place field. Consistent with in vitro showing shift from somatic to dendritic inhibition across place fields.

Silencing SOM interneurons (dendritic targeting) greatly increased the probability of pyramidal cell burst firing. Modest effect on burst from PV (somatic targeting) interneurons.
Silencing PV interneurons have a strong effect on spike-timing and theta-phase relationships than SOM interneurons. Spikes were more gathered toward the trough of theta, reducing phase difference between onset and peak of place fields by more than half.

Combination of Gamma and Theta regulate the phase-precession of pyramids. (Where does theta come from?).

Monday, August 6, 2012

Gain control by layer six in cortical circuits of vision

Olsen, SR. Bortone, DS. Adesnik, H. Scanziani, M. (2012) Gain control by layer six in cortical circuits of vision. Nature 483: 47-54.

This is from Massimo's lab. He is on my committee and one of my favorite scientists. Shawn Olsen is a friend, he's also really good.

L6 excitatory neurons project to superficial layers and primary sensory thalamic nuclei.

They target L6 excitatory neurons with genetic line, approx 65% of population is labeled. L6 neurons can be divided into two categories: apical dendrites that end in L4, and apical dendrites that end in L1. Project to the dLGN and nucleus reticularis (NRT). Non-expressing cells were morphologically distinct. Expressed ChR2, linear multi-channel probe to record across vertical depth.

Drifting gratings. Activation of L6 supressed activity throughout depth, reduced spontaneous activity. Photostimulation scaled the tuning curve (multiplicative gain), did not change preferred orientation, tuning width, or the orientation selectivity index in all other layers. Also photosuppressed L6 with Halo-rhodopsin and the tuning curves scaled up.

Remember that they are activating a big chunk of the population of L6 with light. So this scaling results when a large amount of the population is being excited. The suppression is a good control, as they are turning off the L6 neurons that are active for the stimulation. This is still very broad, however.

L6 activation suppresses activity in dLGN. Mainly through disynaptic inhibition onto dLGN relay neurons - recruitment of NRT inhibitory neurons and possibly local inhibitory interneurons in dLGN. Suppression of cortex as a whole (by photostimulating PV inhibitory neurons), strongly facilitated dLGN. (This negative feedback from cortex to dLGN is part of the idea that cortex is building a model, and sending back to LGN what it thinks the pixels should look like based on the model. This suppresses LGN in all of the places in which cortex's model is correctly predicting the pixels. The errors of the prediction are sent back to cortex to refine the model, and also will signal for plasticity).

L6 can inhibit all layers of cortex through local circuits without going through LGN. Mainly targets neurons within column, falls off as stimulation moves away from target neuron tangentially.

They estimated the transfer function from dLGN to V1 activity to assess the relative inhibitory effects of the two pathways. If the suppression of V1 is larger than predicted by this transfer function, then the rest of the suppression can be attributed to the local circuit.

So, a big key to how cortex works is the feedback control of multiplicative-like inhibition and additive-like excitation. The excitatory neurons represent the information through their tuning curves, and the population is normalized by the inhibitory neurons. The mulitplicative normalization allows the neurons to have the same population-code, but can stabilize the activity across cortex.

Thursday, August 2, 2012

Feedforward, feedback, and inhibitory connections in primate visual cortex

Callaway, EM. (2004) Feedforward, feedback and inhibitory connections in primate visual cortex. Neural Networks 17: 625-632.

Ed Callaway is a prof at Salk, this is who Tina used to work for.

The termination layer of afferents establishes the hierarchical order of cortical areas. Feedforward connections originate in the superficial or superficial and deep layers, lateral connections terminate in all layers and feedback connections terminate in superficial and deep layers. From Felleman and Van Essen (1991) (I'll review this paper later, its a classic):

Two step process feedforward goes to layer 4, layer 4 mainly projects to L2-3, then off to next cortical region. Layer 6 acts modulatory and feeds back mainly on layer 4. Layer 5 does the same for layer 2-3:
Based on the lack of specificity of the connections from deep layers he infers that the deep layers are mainly modulatory on the superficial layers. There's a scanziani paper that shows how layer 6 modulates gain of the other layers, another paper to review. This is an interesting take on the seperate function of the two sheets of cortex. Remember that the superficial cortex is more pattern-completing (lots of feedback connections). 

Cortical areas can also interact by going through thalamus. Each cortical area has a thalamus (roughly), and lower cortical areas will project up to the next level of thalamus. The higher level of thalamus will then project to higher cortical areas. This continues up the hierarchy. So there is both a cortical and thalamic hierarchy where information spirals up. (This is like basal ganglia as well). 

There are some shortcuts from thalamus to higher cortical areas. LGN signals may be able to skip layer 4 and layer 2/3 pathway, and route through layer 6 directly to higher areas. This may be special for vision, as the evidence for this shortcut is for motion signals (V1-L6 to MT). This shortcut may be necessary to process motion signals faster, as that would be extremely important.

Fast-spiking basket cells (aka PV cells) control gain. Feedforward connections from thalamus to L4 and from L4 to L2/3 connect strongly to FS cells. Most other inhibitory types are not targeted by feedforward excitation. Mainly target soma and proximal dendrites.

Somatostatin inhibitory neurons (SOM, Martinotti in cortex) inhibit distal apical tufts of pyramidal cells.

Calretinin neurons could be acting as gating cell. They disinhibit L2/3 while simultaneously inhibiting deep pyramids. Could be switching between classical cortico-cortico pathway and cortico-thalamic-cortico alternative pathway. Direct inhibitory synapses onto deep pyramids. Double negative synapses through other inhibitory interneurons for superficial pyramids.

Wednesday, August 1, 2012

The Subventricular Zone Is the Developmental Milestone of a 6-Layered Neocortex: Comparisons of Metatherian and Eutherian Mammals

Cheung, AFP. Kondo, S. Abdel-Mannan, O. Chodroff, RA. Sirey, TM. Bluy, LE. Webber, N. DeProto, J. Karlen, SJ. Krubitzer, L. Stolp, HB. Saunders, NR. Molnar, Z. (2010) The Subventricular Zone Is the Developmental Milestone of a 6-Layered Neocortex: Comparisons in Metatherian and Eutherian Mammals. Cerebral Cortex 20:1071-1081.

Tangential migration hypothesis only applies to inhibitory neurons. Little evidence for tangential migration of projection neurons. Radial expansion from a 3 to 6-layerd neocortex is more likely due to increased prgenitor population.

Mammals have an SVZ where intermediate progenitor cells form to amplify neuron production. 2-step pattern could facilitate the formation of 6-layer cortex.Sauropsids have 3-layered cortex and lack an organized SVZ.

Marsupials (wallaby and opposum) have fewer total neurons in a normalized cortical column than placentals (mouse). However, they have similar features of SVZ, and still generate a 6-layer cortex. Several independent approaches: 1) H3 staining for mitotic cells in SVZ, 2) Nissl and H&E staining for morphology, 3) VZ/SVZ-specific mRNA expression, and 4) vascular patterns. Results suggests that like all other mammals, marsupials have SVZ with IPCs during cortical development.

The reason that this is important is that the SVZ may be serving as a copy of the VZ, where each progenitor population effectively produces a 3-layer cortex. This has different implications about the organization of cortex from Karten's work. If 6-layer cortex is really 3-layer cortex merged with the BZR (a different unit), then these are fundamentally different circuits. The BZR circuit has tangentially migrated to make 6-layer cortex. However, the duplicate progenitor in SVZ suggests that 6-layer cortex is two 3-layer cortices stacked on top of each other.

It would be interesting to figure out whether neurons from the SVZ migrate to the outer or inner layers specifically, while neurons from VZ migrate to the other layers. I know that there is an order to the migration pattern (I forget if its outside in or inside out), so if SVZ comes on later then the neurons born there will be in the same layers.